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Mlops Machine Learning Engineer Jobs in Denton, TX

As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting-edge machine learning models and solutions to enhance various aspects of our business operations, from ...

R249230Lead Machine Learning Engineer**Join the Dealer Tech division within Capital One's Financial Services Technology group, where we develop and support cutting edge technological solutions that ...

New

Lead Machine Learning Engineer Join the Dealer Tech division within Capital One's Financial Services Technology group, where we develop and support cutting edge technological solutions that ...

New

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $129K/yr

Lead Machine Learning Engineer Join the Dealer Tech division within Capital One's Financial Services Technology group, where we develop and support cutting edge technological solutions that ...

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $129K/yr

Lead Machine Learning Engineer Join the Dealer Tech division within Capital One's Financial Services Technology group, where we develop and support cutting edge technological solutions that ...

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Lead Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing Generative AI and advanced agentic systems at scale.

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Lead Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing Generative AI and advanced agentic systems at scale.

Machine Learning Engineer

Plano, TX · On-site

$62K - $100K/yr

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

Machine Learning Engineer

Dallas, TX · On-site

$62K - $100K/yr

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

MLOps Engineer Location: Grapevine, TX & Dallas, TX - (Hybrid) Duration: 6+ Months Contract Key ... of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, SQL, Big Data, GCP, and Shell scripting.

Machine Learning Operations Engineer

Dallas, TX · On-site

$68K - $93K/yr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC ... Work with AWS and SageMaker MLOps ecosystem. Requirements * 6+ years of experience in software ...

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work closely with product and ...

Machine Learning Operations Engineer

Dallas, TX · On-site

$68K - $93K/yr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC ... Work with AWS and SageMaker MLOps ecosystem. Requirements * 6+ years of experience in software ...

Machine Learning Engineer - NJ

Addison, TX

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work closely with product and ...

Machine Learning Operations Engineer

Dallas, TX · On-site

$68K - $93K/yr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC ... Work with AWS and SageMaker MLOps ecosystem. Requirements * 6+ years of experience in software ...

Showing results 21-40

Mlops Machine Learning Engineer information

See Denton, TX salary details

$29.5K

$120.7K

$181.4K

How much do mlops machine learning engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for mlops machine learning engineer in Denton, TX is $120,735.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,200.00 and $145,300.00 per year, depending on experience, location, and employer.

What does an MLOps machine learning engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

What are the key skills and qualifications needed to thrive as an MLOps machine learning engineer?

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.

How does an MLOps machine learning engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

Are MLOps machine learning engineers in demand?

MLOps machine learning engineers are in high demand due to the increasing adoption of AI and machine learning across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects and competitive salaries.

Do MLOps Machine Learning Engineers need a degree?

MLOps Machine Learning Engineers typically do not require a formal degree but often have a background in computer science, data science, or related fields. Practical skills in machine learning, cloud platforms, and tools like Docker, Kubernetes, and CI/CD pipelines are highly valued. Certifications and hands-on experience can also enhance job prospects.

What are popular job titles related to Mlops Machine Learning Engineer jobs in Denton, TX?

For Mlops Machine Learning Engineer jobs in Denton, TX, the most frequently searched job titles are:

What job categories do people searching Mlops Machine Learning Engineer jobs in Denton, TX look for?

The top searched job categories for Mlops Machine Learning Engineer jobs in Denton, TX are:

What cities near Denton, TX are hiring for Mlops Machine Learning Engineer jobs?

Cities near Denton, TX with the most Mlops Machine Learning Engineer job openings:

Infographic showing various Mlops Machine Learning Engineer job openings in Denton, TX as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 22% Part Time, and 1% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $120,735 per year, or $58 per hour.

Machine Learning Engineer II

Yum! Brands

Plano, TX

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 5 days ago


Yum! Brands rating

5.4

Company rating: 5.4 out of 10

Based on 13 frontline employees who took The Breakroom Quiz


Job description

Hybrid onsite requirement in either Plano, TX - Irvine, CA - Louisville, KY

Company Overview:

Yum Brands is a global leader in the fast-food industry, with a portfolio of renowned brands including KFC, Pizza Hut, Taco Bell, and more. We're dedicated to providing delicious, convenient, and innovative food experiences to our customers worldwide.

Position Overview:

We are seeking a talented and passionate Machine Learning Engineer to join our dynamic team at Yum Brands. As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting-edge machine learning models and solutions to enhance various aspects of our business operations, from customer experience optimization to supply chain management.

Qualifications:

  • Bachelor's or master's degree in computer science, engineering, mathematics, or a related field.
  • Proven experience (4+ years) in developing and deploying in production environments, preferably in the context of real-world business applications.
  • Proficiency in Python with strong software engineering skills and experience in building scalable and maintainable code.
  • Proficiency in message queue technologies and services like Kafka, Pulsar, or RabbitMQ and experience working with real-time data streaming.
  • Experience working with containerization technologies such as Docker and Kubernetes.
  • Strong analytical and problem-solving skills, with the ability to translate business requirements into technical solutions.
  • Excellent communication and collaboration skills, with the ability to work effectively in a fast-paced and dynamic environment.

Nice to Have:

  • Familiarity with latest tools and trends surrounding Large Language Models and Generative AI.
  • Experience with cloud computing platforms such as AWS, Azure, or GCP.
  • Experience with version control systems, such as Git.

Salary Range: 105,500 - 132,200

Benefits: Employees (and their eligible family members) may enroll in the following types of insurance coverage: medical, dental, vision, legal, and accidental death and dismemberment, as well as FSA/HSA (depending on enrolled medical plan). Yum! also provides short-term disability, long-term disability, and life insurance. Employees may enroll in our 401(k) plan. Yum! provides 4 weeks of vacation, paid sick leave, 10 paid holidays, a floating day off, half day Fridays year-round and 2 paid days for volunteer time each calendar year. To learn more about working at Yum! -Click here. 

At Yum!, one of our core values is to Believe in ALL People. This means seeing the value in everyone and unlocking their full potential to be their best self. YUM! Brands, Inc. (including its subsidiaries Yum Restaurant Services Group, LLC ("YRSG") and Yum Connect, LLC ("Yum Digital and Technology")(collectively, "Yum") is proud to be an equal opportunity employer and is committed to equity, inclusion, and belonging for all dimensions of diversity.  We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other protected characteristic. Yum! is committed to working with and providing reasonable accommodation to applicants with disabilities or special needs.

US Job Seekers/Employees - Click here to view the "Know Your Rights" poster and supplement and the Pay Transparency Policy Statement.

Qualifications:

  • Bachelor's or master's degree in computer science, engineering, mathematics, or a related field.
  • Proven experience (4+ years) in developing and deploying in production environments, preferably in the context of real-world business applications.
  • Proficiency in Python with strong software engineering skills and experience in building scalable and maintainable code.
  • Proficiency in message queue technologies and services like Kafka, Pulsar, or RabbitMQ and experience working with real-time data streaming.
  • Experience working with containerization technologies such as Docker and Kubernetes.
  • Strong analytical and problem-solving skills, with the ability to translate business requirements into technical solutions.
  • Excellent communication and collaboration skills, with the ability to work effectively in a fast-paced and dynamic environment.

Nice to Have:

  • Familiarity with latest tools and trends surrounding Large Language Models and Generative AI.
  • Experience with cloud computing platforms such as AWS, Azure, or GCP.
  • Experience with version control systems, such as Git.

Key Responsibilities:

  • Collaborate with cross-functional teams including data scientists, software engineers, and business stakeholders to identify opportunities for leveraging machine learning techniques to drive business outcomes.
  • Design, develop, and deploy scalable machine learning models and algorithms that address business challenges and improve operational efficiency.
  • Optimize machine learning models for performance, scalability, and efficiency.

  • Build robust data pipelines and infrastructure to support the training and deployment of machine learning models in production environments.
  • Work with DevOps teams to automate deployment processes, monitor system performance, and ensure the smooth operation of applications and services in production.
  • Stay updated on emerging technologies and industry trends in machine learning, software engineering, and cloud computing, and evaluate their potential impact on our business operations.

What Yum! Brands employees say

Pay

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Hours and flexibility

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